Development of Action-Recognition Technology Using LSTM Based on Skeleton Data

Recently, the population of workers of adults aged over 55 years is growing at work sites. Middle-aged adult workers have a higher occurrence rate of work accidents than younger workers. Therefore, it is necessary to develop a safety-management system to ensure safety. This paper proposes an approach for the recognition of human actions based on human-skeleton data as a part of the system in construction industries. The proposed approach consists of four processes: ⅰ) extraction of skeleton data from captured video data, ⅱ) interpolation of skeleton joint-points that were missed, ⅲ) calculation of features using interpolated skeleton data, and ⅳ) construction of action-recognition model using interpolated data and calculated features. We evaluated the action-recognition accuracy performance for 5 types of actions from 6 subjects. The evaluation result achieved a high recognition accuracy of 93.1% on average. The results reveal that the proposed approach can be used to recognize actions from video data, and interpolation methods can significantly improve the action-recognition accuracy of the proposed approach.